
#Importing libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns


import warnings
warnings.filterwarnings('ignore')


df=pd.read_csv('data.csv')
df


df.head(5)


df.shape #569 rows and 33 columns

(569, 33)


df.isnull().sum() #checking for null values in every column

id                           0
diagnosis                    0
radius_mean                  0
texture_mean                 0
perimeter_mean               0
area_mean                    0
smoothness_mean              0
compactness_mean             0
concavity_mean               0
concave points_mean          0
symmetry_mean                0
fractal_dimension_mean       0
radius_se                    0
texture_se                   0
perimeter_se                 0
area_se                      0
smoothness_se                0
compactness_se               0
concavity_se                 0
concave points_se            0
symmetry_se                  0
fractal_dimension_se         0
radius_worst                 0
texture_worst                0
perimeter_worst              0
area_worst                   0
smoothness_worst             0
compactness_worst            0
concavity_worst              0
concave points_worst         0
symmetry_worst               0
fractal_dimension_worst      0
Unnamed: 32                569
dtype: int64


#Deleting the unnamed column because all its values are null values
df=df.dropna(axis=1)


df.shape

(569, 32)


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 569 entries, 0 to 568
Data columns (total 32 columns):
 #   Column                   Non-Null Count  Dtype  
---  ------                   --------------  -----  
 0   id                       569 non-null    int64  
 1   diagnosis                569 non-null    object 
 2   radius_mean              569 non-null    float64
 3   texture_mean             569 non-null    float64
 4   perimeter_mean           569 non-null    float64
 5   area_mean                569 non-null    float64
 6   smoothness_mean          569 non-null    float64
 7   compactness_mean         569 non-null    float64
 8   concavity_mean           569 non-null    float64
 9   concave points_mean      569 non-null    float64
 10  symmetry_mean            569 non-null    float64
 11  fractal_dimension_mean   569 non-null    float64
 12  radius_se                569 non-null    float64
 13  texture_se               569 non-null    float64
 14  perimeter_se             569 non-null    float64
 15  area_se                  569 non-null    float64
 16  smoothness_se            569 non-null    float64
 17  compactness_se           569 non-null    float64
 18  concavity_se             569 non-null    float64
 19  concave points_se        569 non-null    float64
 20  symmetry_se              569 non-null    float64
 21  fractal_dimension_se     569 non-null    float64
 22  radius_worst             569 non-null    float64
 23  texture_worst            569 non-null    float64
 24  perimeter_worst          569 non-null    float64
 25  area_worst               569 non-null    float64
 26  smoothness_worst         569 non-null    float64
 27  compactness_worst        569 non-null    float64
 28  concavity_worst          569 non-null    float64
 29  concave points_worst     569 non-null    float64
 30  symmetry_worst           569 non-null    float64
 31  fractal_dimension_worst  569 non-null    float64
dtypes: float64(30), int64(1), object(1)
memory usage: 142.4+ KB


#Getting a count of the number of malignant and benign cells
df['diagnosis'].value_counts()
#B: Benign means not cancerous
#M: Malignant means cancerous

B    357
M    212
Name: diagnosis, dtype: int64


#Visualize the count
sns.countplot(df['diagnosis'],label='count')

<AxesSubplot:xlabel='diagnosis', ylabel='count'>


#Checking for categorical variables
categorical=df.select_dtypes(include='object').keys()
categorical

Index(['diagnosis'], dtype='object')


from sklearn.preprocessing import LabelEncoder
labelencoder_Y=LabelEncoder()
labelencoder_Y.fit_transform(df.iloc[:,1].values)

array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
       1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1,
       0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1,
       0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0,
       0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1,
       1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0,
       0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1,
       1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1,
       0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0,
       0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1,
       1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1,
       1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1,
       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0,
       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0,
       0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
       0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0,
       0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0])


df.iloc[:,1]=labelencoder_Y.fit_transform(df.iloc[:,1].values)
df.iloc[:,1]

0      1
1      1
2      1
3      1
4      1
      ..
564    1
565    1
566    1
567    1
568    0
Name: diagnosis, Length: 569, dtype: int32


sns.pairplot(df,hue='diagnosis')

<seaborn.axisgrid.PairGrid at 0x2d9624eeac0>


sns.pairplot(df.iloc[:,1:5],hue='diagnosis')

<seaborn.axisgrid.PairGrid at 0x2d97b798610>


df.describe()


df.isnull().sum().sum()#Checking for any null values still left in the dataset

0


#counterplot of feature mean radius
plt.figure(figsize=(20,5))
sns.countplot(df['radius_mean'])

<AxesSubplot:xlabel='radius_mean', ylabel='count'>


df.corr()


#Heatmap of Correlation matrix 
plt.figure(figsize=(20,20))
sns.heatmap(df.corr(),annot=True,cmap='coolwarm',linewidth=2)

<AxesSubplot:>


#Getting the correlation of the columns
plt.figure(figsize=(10,10))
sns.heatmap(df.iloc[:,1:12].corr(),annot=True,fmt='.0%')

<AxesSubplot:>


#splitting the dataset into train and test set
#Splitting into independent(X) and dependent(y)
X=df.iloc[:,2:31].values
y=df.iloc[:,1].values


#Splitting the dataset into 75% training and 25%testing
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.25,random_state=0)


#Scale the data(Feature Scaling)
from sklearn.preprocessing import StandardScaler
sc=StandardScaler()
X_train=sc.fit_transform(X_train)
X_test=sc.fit_transform(X_test)


#Create a function for the models
def models(X_train,y_train):
    #Logistic Regression
    from sklearn.linear_model import LogisticRegression
    log=LogisticRegression(random_state=0)
    log.fit(X_train,y_train)
    
    #Decision Tree
    from sklearn.tree import DecisionTreeClassifier
    tree=DecisionTreeClassifier(criterion='entropy',random_state=0)
    tree.fit(X_train,y_train)
    
    #Random Forest Classifier
    from sklearn.ensemble import RandomForestClassifier
    forest=RandomForestClassifier(criterion='entropy', max_features='sqrt',
                       min_samples_split=3, n_estimators=20, random_state=0)
    forest.fit(X_train,y_train)
    
    #Support Vector Classifier
    from sklearn.svm import SVC
    svc_classifier=SVC(C=1,kernel='linear')
    svc_classifier.fit(X_train,y_train)
    
    #K-Nearest Neighbor Classifier
    from sklearn.neighbors import KNeighborsClassifier
    knn=KNeighborsClassifier(n_neighbors=5,metric='minkowski',p=2)
    knn.fit(X_train,y_train)
    
    #Naive Bayes Classifier
    from sklearn.naive_bayes import GaussianNB
    nb_classifier=GaussianNB()
    nb_classifier.fit(X_train,y_train)
    
    #Adaboost Classifier
    from sklearn.ensemble import AdaBoostClassifier
    adb_classifier=AdaBoostClassifier(DecisionTreeClassifier(criterion='entropy'))
    adb_classifier.fit(X_train,y_train)
    
    #XGBoost Classifier
    from xgboost import XGBClassifier
    xgb_classifier=XGBClassifier()
    xgb_classifier.fit(X_train,y_train)
    
    #Print the models accuracy on the training data
    print('[0]Logistic Regression Training Accuracy: ',log.score(X_train,y_train))
    print('[1]Decision Tree Classifier Training Accuracy: ',tree.score(X_train,y_train))
    print('[2]Random Forest Classifier Training Accuracy: ',forest.score(X_train,y_train))
    print('[3]Support Vector Classifier Training Accuracy: ',svc_classifier.score(X_train,y_train))
    print('[4]K-Nearest Neighbor Classifier Training Accuracy: ',knn.score(X_train,y_train))
    print('[5]Naive Bayes Classifier Training Accuracy: ',nb_classifier.score(X_train,y_train))
    print('[6]AdaBoost Classifier Training Accuracy: ',adb_classifier.score(X_train,y_train))
    print('[7]XGBoost Classifier Training Accuracy: ',xgb_classifier.score(X_train,y_train))
    
    
    return log,tree,forest,svc_classifier,knn,nb_classifier,adb_classifier,xgb_classifier


#Getting all of the models
model=models(X_train,y_train)

[00:36:16] WARNING: ..\src\learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[0]Logistic Regression Training Accuracy:  0.9906103286384976
[1]Decision Tree Classifier Training Accuracy:  1.0
[2]Random Forest Classifier Training Accuracy:  1.0
[3]Support Vector Classifier Training Accuracy:  0.9882629107981221
[4]K-Nearest Neighbor Classifier Training Accuracy:  0.9765258215962441
[5]Naive Bayes Classifier Training Accuracy:  0.9507042253521126
[6]AdaBoost Classifier Training Accuracy:  1.0
[7]XGBoost Classifier Training Accuracy:  1.0


#Testing model accuracy on test data on confusion matrix
from sklearn.metrics import confusion_matrix
for i in range(len(model)):
    print('Model',i)
    cm=confusion_matrix(y_test,model[i].predict(X_test))
    TP=cm[0][0]
    TN=cm[1][1]
    FN=cm[1][0]
    FP=cm[0][1]

    print("The Confusion Matrix is \n",cm)
    print('Testing Accuracy = ',(TP+TN)/(TP+TN+FP+FN))
    print()

Model 0
The Confusion Matrix is 
 [[86  4]
 [ 3 50]]
Testing Accuracy =  0.951048951048951

Model 1
The Confusion Matrix is 
 [[83  7]
 [ 2 51]]
Testing Accuracy =  0.9370629370629371

Model 2
The Confusion Matrix is 
 [[87  3]
 [ 1 52]]
Testing Accuracy =  0.972027972027972

Model 3
The Confusion Matrix is 
 [[86  4]
 [ 2 51]]
Testing Accuracy =  0.958041958041958

Model 4
The Confusion Matrix is 
 [[89  1]
 [ 6 47]]
Testing Accuracy =  0.951048951048951

Model 5
The Confusion Matrix is 
 [[87  3]
 [ 5 48]]
Testing Accuracy =  0.9440559440559441

Model 6
The Confusion Matrix is 
 [[84  6]
 [ 2 51]]
Testing Accuracy =  0.9440559440559441

Model 7
The Confusion Matrix is 
 [[88  2]
 [ 2 51]]
Testing Accuracy =  0.972027972027972


#Getting precision,recall,f1-score,support of the models
from sklearn.metrics import classification_report
from sklearn.metrics import accuracy_score
for i in range(len(model)):
    print('Model',i)
    print(classification_report(y_test,model[i].predict(X_test)))
    print(accuracy_score(y_test,model[i].predict(X_test)))
    print()

Model 0
              precision    recall  f1-score   support

           0       0.97      0.96      0.96        90
           1       0.93      0.94      0.93        53

    accuracy                           0.95       143
   macro avg       0.95      0.95      0.95       143
weighted avg       0.95      0.95      0.95       143

0.951048951048951

Model 1
              precision    recall  f1-score   support

           0       0.98      0.92      0.95        90
           1       0.88      0.96      0.92        53

    accuracy                           0.94       143
   macro avg       0.93      0.94      0.93       143
weighted avg       0.94      0.94      0.94       143

0.9370629370629371

Model 2
              precision    recall  f1-score   support

           0       0.99      0.97      0.98        90
           1       0.95      0.98      0.96        53

    accuracy                           0.97       143
   macro avg       0.97      0.97      0.97       143
weighted avg       0.97      0.97      0.97       143

0.972027972027972

Model 3
              precision    recall  f1-score   support

           0       0.98      0.96      0.97        90
           1       0.93      0.96      0.94        53

    accuracy                           0.96       143
   macro avg       0.95      0.96      0.96       143
weighted avg       0.96      0.96      0.96       143

0.958041958041958

Model 4
              precision    recall  f1-score   support

           0       0.94      0.99      0.96        90
           1       0.98      0.89      0.93        53

    accuracy                           0.95       143
   macro avg       0.96      0.94      0.95       143
weighted avg       0.95      0.95      0.95       143

0.951048951048951

Model 5
              precision    recall  f1-score   support

           0       0.95      0.97      0.96        90
           1       0.94      0.91      0.92        53

    accuracy                           0.94       143
   macro avg       0.94      0.94      0.94       143
weighted avg       0.94      0.94      0.94       143

0.9440559440559441

Model 6
              precision    recall  f1-score   support

           0       0.98      0.93      0.95        90
           1       0.89      0.96      0.93        53

    accuracy                           0.94       143
   macro avg       0.94      0.95      0.94       143
weighted avg       0.95      0.94      0.94       143

0.9440559440559441

Model 7
              precision    recall  f1-score   support

           0       0.98      0.98      0.98        90
           1       0.96      0.96      0.96        53

    accuracy                           0.97       143
   macro avg       0.97      0.97      0.97       143
weighted avg       0.97      0.97      0.97       143

0.972027972027972


# Parameter Tuning Randomized Search Random Forest Classifier Model
from sklearn.ensemble import RandomForestClassifier
forest1=RandomForestClassifier(random_state=0)
from sklearn.model_selection import RandomizedSearchCV
rs1=RandomizedSearchCV(forest1,
{
    'n_estimators':[10,20,30,40,50,60,70,80,90,100],
    'criterion':['gini','entropy'],
    'min_samples_split': [2,3,4,5,6,7,8,9,10], 
    'max_features' : ['auto', 'sqrt', 'log2']
},
cv=5,
return_train_score=False,
n_iter=3
)
rs1.fit(X_train,y_train)
pd.DataFrame(rs1.cv_results_)[['param_n_estimators','param_criterion','param_min_samples_split','param_max_features','mean_test_score']]


params={
    "learning_rate":[0.05,0.10,0,15,0.20,0.25,0.30],
    "max_depth":[3,4,5,6,8,10,12,15],
    "min_child_weight":[1,3,5,7],
    "gamma":[0.0,0.1,0.2,0.3,0.4],
    "colsample_bytree":[0.3,0.4,0.5,0.7]
}


from xgboost import XGBClassifier
xgb1=XGBClassifier()
from sklearn.model_selection import RandomizedSearchCV
rs2=RandomizedSearchCV(xgb1,param_distributions=params,scoring='roc_auc',n_jobs=-1,verbose=3)

rs2.fit(X_train,y_train)

Fitting 5 folds for each of 10 candidates, totalling 50 fits

[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.
[Parallel(n_jobs=-1)]: Done  16 tasks      | elapsed:    4.0s

[00:16:21] WARNING: ..\src\learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.

[Parallel(n_jobs=-1)]: Done  50 out of  50 | elapsed:    4.8s finished

RandomizedSearchCV(estimator=XGBClassifier(base_score=None, booster=None,
                                           colsample_bylevel=None,
                                           colsample_bynode=None,
                                           colsample_bytree=None, gamma=None,
                                           gpu_id=None, importance_type='gain',
                                           interaction_constraints=None,
                                           learning_rate=None,
                                           max_delta_step=None, max_depth=None,
                                           min_child_weight=None, missing=nan,
                                           monotone_constraints=None,
                                           n_estimators=100, n_job...
                                           random_state=None, reg_alpha=None,
                                           reg_lambda=None,
                                           scale_pos_weight=None,
                                           subsample=None, tree_method=None,
                                           validate_parameters=None,
                                           verbosity=None),
                   n_jobs=-1,
                   param_distributions={'colsample_bytree': [0.3, 0.4, 0.5,
                                                             0.7],
                                        'gamma': [0.0, 0.1, 0.2, 0.3, 0.4],
                                        'learning_rate': [0.05, 0.1, 0, 15, 0.2,
                                                          0.25, 0.3],
                                        'max_depth': [3, 4, 5, 6, 8, 10, 12,
                                                      15],
                                        'min_child_weight': [1, 3, 5, 7]},
                   scoring='roc_auc', verbose=3)


rs2.best_params_

{'min_child_weight': 1,
 'max_depth': 12,
 'learning_rate': 0.2,
 'gamma': 0.0,
 'colsample_bytree': 0.3}


xg=XGBClassifier(use_label_encoder=False,base_score=None, booster=None,
                                           colsample_bylevel=None,
                                           colsample_bynode=None,
                                           colsample_bytree=0.5, gamma=0.0,
                                           gpu_id=None, importance_type='gain',
                                           interaction_constraints=None,
                                           learning_rate=0.05,
                                           max_delta_step=1, max_depth=10,
                                           min_child_weight=None,
                                           monotone_constraints=None,
                                           n_estimators=100,
                                           random_state=None, reg_alpha=None,
                                           reg_lambda=None,
                                           scale_pos_weight=None,
                                           subsample=None, tree_method=None,
                                           validate_parameters=None,
                                           verbosity=None)


xg.fit(X_train,y_train)
xg.score(X_test,y_test)

[00:16:21] WARNING: ..\src\learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.

0.9790209790209791


y_pred=xg.predict(X_test)
print(classification_report(y_test,y_pred))
print(accuracy_score(y_test,y_pred))

              precision    recall  f1-score   support

           0       1.00      0.97      0.98        90
           1       0.95      1.00      0.97        53

    accuracy                           0.98       143
   macro avg       0.97      0.98      0.98       143
weighted avg       0.98      0.98      0.98       143

0.9790209790209791


#Predicted value of XGBoost Classifier
y_pred=xg.predict(X_test)
print(y_pred)

[1 0 0 0 0 0 0 0 0 0 1 0 0 1 1 1 0 1 1 1 1 1 0 0 1 0 0 1 0 1 0 1 0 1 0 1 0
 1 0 1 1 0 1 0 0 1 0 0 0 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 1 0 1 1 1 0 0 1 0 1
 1 0 0 0 0 0 1 1 1 0 1 0 0 0 1 1 0 1 0 1 0 0 1 0 0 0 0 0 0 0 1 0 1 0 1 1 0
 1 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 1]


print(y_test)

[1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 0 0 1 0 0 1 0 1 0 1 0 1 0 1 0
 1 0 1 1 0 1 0 0 1 0 0 0 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 1 0 1 1 1 0 0 1 0 1
 1 0 0 0 0 0 1 1 1 0 1 0 0 0 1 1 0 1 0 1 0 0 1 0 0 0 0 0 0 0 1 0 1 0 1 1 0
 1 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 1]


cm=confusion_matrix(y_test,y_pred)
plt.title('Heatmap of Confusion Matrix',fontsize=15)
sns.heatmap(cm,annot=True)
plt.show()


from sklearn.model_selection import cross_val_score
cross_validation=cross_val_score(estimator=xg,X=X_train,y=y_train,cv=10)
print("Cross validation mean accuracy of XGBoost Classifier= ",cross_validation.mean())

[00:16:21] WARNING: ..\src\learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[00:16:21] WARNING: ..\src\learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[00:16:21] WARNING: ..\src\learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[00:16:21] WARNING: ..\src\learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[00:16:22] WARNING: ..\src\learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[00:16:22] WARNING: ..\src\learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[00:16:22] WARNING: ..\src\learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[00:16:22] WARNING: ..\src\learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[00:16:22] WARNING: ..\src\learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
[00:16:22] WARNING: ..\src\learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
Cross validation mean accuracy of XGBoost Classifier=  0.9578073089700997


import pickle
#Saving model
pickle.dump(xg,open('Breast_Cancer_Detection_MiniProject.pickle','wb'))
#Loading model
breast_cancer_detection_model=pickle.load(open('Breast_Cancer_Detection_MiniProject.pickle','rb'))
#Predicting the output
y_pred=breast_cancer_detection_model.predict(X_test)
#Confusion Matrix
print('Confusion Matrix of XGBoost Model: \n',confusion_matrix(y_test,y_pred),'\n')
#Show the accuracy
print('Accuracy of XGBoost Model = ',accuracy_score(y_test,y_pred))

Confusion Matrix of XGBoost Model: 
 [[87  3]
 [ 0 53]] 

Accuracy of XGBoost Model =  0.9790209790209791

	id	diagnosis	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	...	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst	Unnamed: 32
0	842302	M	17.99	10.38	122.80	1001.0	0.11840	0.27760	0.30010	0.14710	...	17.33	184.60	2019.0	0.16220	0.66560	0.7119	0.2654	0.4601	0.11890	NaN
1	842517	M	20.57	17.77	132.90	1326.0	0.08474	0.07864	0.08690	0.07017	...	23.41	158.80	1956.0	0.12380	0.18660	0.2416	0.1860	0.2750	0.08902	NaN
2	84300903	M	19.69	21.25	130.00	1203.0	0.10960	0.15990	0.19740	0.12790	...	25.53	152.50	1709.0	0.14440	0.42450	0.4504	0.2430	0.3613	0.08758	NaN
3	84348301	M	11.42	20.38	77.58	386.1	0.14250	0.28390	0.24140	0.10520	...	26.50	98.87	567.7	0.20980	0.86630	0.6869	0.2575	0.6638	0.17300	NaN
4	84358402	M	20.29	14.34	135.10	1297.0	0.10030	0.13280	0.19800	0.10430	...	16.67	152.20	1575.0	0.13740	0.20500	0.4000	0.1625	0.2364	0.07678	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
564	926424	M	21.56	22.39	142.00	1479.0	0.11100	0.11590	0.24390	0.13890	...	26.40	166.10	2027.0	0.14100	0.21130	0.4107	0.2216	0.2060	0.07115	NaN
565	926682	M	20.13	28.25	131.20	1261.0	0.09780	0.10340	0.14400	0.09791	...	38.25	155.00	1731.0	0.11660	0.19220	0.3215	0.1628	0.2572	0.06637	NaN
566	926954	M	16.60	28.08	108.30	858.1	0.08455	0.10230	0.09251	0.05302	...	34.12	126.70	1124.0	0.11390	0.30940	0.3403	0.1418	0.2218	0.07820	NaN
567	927241	M	20.60	29.33	140.10	1265.0	0.11780	0.27700	0.35140	0.15200	...	39.42	184.60	1821.0	0.16500	0.86810	0.9387	0.2650	0.4087	0.12400	NaN
568	92751	B	7.76	24.54	47.92	181.0	0.05263	0.04362	0.00000	0.00000	...	30.37	59.16	268.6	0.08996	0.06444	0.0000	0.0000	0.2871	0.07039	NaN

	id	diagnosis	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	...	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst	Unnamed: 32
0	842302	M	17.99	10.38	122.80	1001.0	0.11840	0.27760	0.3001	0.14710	...	17.33	184.60	2019.0	0.1622	0.6656	0.7119	0.2654	0.4601	0.11890	NaN
1	842517	M	20.57	17.77	132.90	1326.0	0.08474	0.07864	0.0869	0.07017	...	23.41	158.80	1956.0	0.1238	0.1866	0.2416	0.1860	0.2750	0.08902	NaN
2	84300903	M	19.69	21.25	130.00	1203.0	0.10960	0.15990	0.1974	0.12790	...	25.53	152.50	1709.0	0.1444	0.4245	0.4504	0.2430	0.3613	0.08758	NaN
3	84348301	M	11.42	20.38	77.58	386.1	0.14250	0.28390	0.2414	0.10520	...	26.50	98.87	567.7	0.2098	0.8663	0.6869	0.2575	0.6638	0.17300	NaN
4	84358402	M	20.29	14.34	135.10	1297.0	0.10030	0.13280	0.1980	0.10430	...	16.67	152.20	1575.0	0.1374	0.2050	0.4000	0.1625	0.2364	0.07678	NaN

	id	diagnosis	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	...	radius_worst	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst
count	5.690000e+02	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	...	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000
mean	3.037183e+07	0.372583	14.127292	19.289649	91.969033	654.889104	0.096360	0.104341	0.088799	0.048919	...	16.269190	25.677223	107.261213	880.583128	0.132369	0.254265	0.272188	0.114606	0.290076	0.083946
std	1.250206e+08	0.483918	3.524049	4.301036	24.298981	351.914129	0.014064	0.052813	0.079720	0.038803	...	4.833242	6.146258	33.602542	569.356993	0.022832	0.157336	0.208624	0.065732	0.061867	0.018061
min	8.670000e+03	0.000000	6.981000	9.710000	43.790000	143.500000	0.052630	0.019380	0.000000	0.000000	...	7.930000	12.020000	50.410000	185.200000	0.071170	0.027290	0.000000	0.000000	0.156500	0.055040
25%	8.692180e+05	0.000000	11.700000	16.170000	75.170000	420.300000	0.086370	0.064920	0.029560	0.020310	...	13.010000	21.080000	84.110000	515.300000	0.116600	0.147200	0.114500	0.064930	0.250400	0.071460
50%	9.060240e+05	0.000000	13.370000	18.840000	86.240000	551.100000	0.095870	0.092630	0.061540	0.033500	...	14.970000	25.410000	97.660000	686.500000	0.131300	0.211900	0.226700	0.099930	0.282200	0.080040
75%	8.813129e+06	1.000000	15.780000	21.800000	104.100000	782.700000	0.105300	0.130400	0.130700	0.074000	...	18.790000	29.720000	125.400000	1084.000000	0.146000	0.339100	0.382900	0.161400	0.317900	0.092080
max	9.113205e+08	1.000000	28.110000	39.280000	188.500000	2501.000000	0.163400	0.345400	0.426800	0.201200	...	36.040000	49.540000	251.200000	4254.000000	0.222600	1.058000	1.252000	0.291000	0.663800	0.207500

	id	diagnosis	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	...	radius_worst	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst
id	1.000000	0.039769	0.074626	0.099770	0.073159	0.096893	-0.012968	0.000096	0.050080	0.044158	...	0.082405	0.064720	0.079986	0.107187	0.010338	-0.002968	0.023203	0.035174	-0.044224	-0.029866
diagnosis	0.039769	1.000000	0.730029	0.415185	0.742636	0.708984	0.358560	0.596534	0.696360	0.776614	...	0.776454	0.456903	0.782914	0.733825	0.421465	0.590998	0.659610	0.793566	0.416294	0.323872
radius_mean	0.074626	0.730029	1.000000	0.323782	0.997855	0.987357	0.170581	0.506124	0.676764	0.822529	...	0.969539	0.297008	0.965137	0.941082	0.119616	0.413463	0.526911	0.744214	0.163953	0.007066
texture_mean	0.099770	0.415185	0.323782	1.000000	0.329533	0.321086	-0.023389	0.236702	0.302418	0.293464	...	0.352573	0.912045	0.358040	0.343546	0.077503	0.277830	0.301025	0.295316	0.105008	0.119205
perimeter_mean	0.073159	0.742636	0.997855	0.329533	1.000000	0.986507	0.207278	0.556936	0.716136	0.850977	...	0.969476	0.303038	0.970387	0.941550	0.150549	0.455774	0.563879	0.771241	0.189115	0.051019
area_mean	0.096893	0.708984	0.987357	0.321086	0.986507	1.000000	0.177028	0.498502	0.685983	0.823269	...	0.962746	0.287489	0.959120	0.959213	0.123523	0.390410	0.512606	0.722017	0.143570	0.003738
smoothness_mean	-0.012968	0.358560	0.170581	-0.023389	0.207278	0.177028	1.000000	0.659123	0.521984	0.553695	...	0.213120	0.036072	0.238853	0.206718	0.805324	0.472468	0.434926	0.503053	0.394309	0.499316
compactness_mean	0.000096	0.596534	0.506124	0.236702	0.556936	0.498502	0.659123	1.000000	0.883121	0.831135	...	0.535315	0.248133	0.590210	0.509604	0.565541	0.865809	0.816275	0.815573	0.510223	0.687382
concavity_mean	0.050080	0.696360	0.676764	0.302418	0.716136	0.685983	0.521984	0.883121	1.000000	0.921391	...	0.688236	0.299879	0.729565	0.675987	0.448822	0.754968	0.884103	0.861323	0.409464	0.514930
concave points_mean	0.044158	0.776614	0.822529	0.293464	0.850977	0.823269	0.553695	0.831135	0.921391	1.000000	...	0.830318	0.292752	0.855923	0.809630	0.452753	0.667454	0.752399	0.910155	0.375744	0.368661
symmetry_mean	-0.022114	0.330499	0.147741	0.071401	0.183027	0.151293	0.557775	0.602641	0.500667	0.462497	...	0.185728	0.090651	0.219169	0.177193	0.426675	0.473200	0.433721	0.430297	0.699826	0.438413
fractal_dimension_mean	-0.052511	-0.012838	-0.311631	-0.076437	-0.261477	-0.283110	0.584792	0.565369	0.336783	0.166917	...	-0.253691	-0.051269	-0.205151	-0.231854	0.504942	0.458798	0.346234	0.175325	0.334019	0.767297
radius_se	0.143048	0.567134	0.679090	0.275869	0.691765	0.732562	0.301467	0.497473	0.631925	0.698050	...	0.715065	0.194799	0.719684	0.751548	0.141919	0.287103	0.380585	0.531062	0.094543	0.049559
texture_se	-0.007526	-0.008303	-0.097317	0.386358	-0.086761	-0.066280	0.068406	0.046205	0.076218	0.021480	...	-0.111690	0.409003	-0.102242	-0.083195	-0.073658	-0.092439	-0.068956	-0.119638	-0.128215	-0.045655
perimeter_se	0.137331	0.556141	0.674172	0.281673	0.693135	0.726628	0.296092	0.548905	0.660391	0.710650	...	0.697201	0.200371	0.721031	0.730713	0.130054	0.341919	0.418899	0.554897	0.109930	0.085433
area_se	0.177742	0.548236	0.735864	0.259845	0.744983	0.800086	0.246552	0.455653	0.617427	0.690299	...	0.757373	0.196497	0.761213	0.811408	0.125389	0.283257	0.385100	0.538166	0.074126	0.017539
smoothness_se	0.096781	-0.067016	-0.222600	0.006614	-0.202694	-0.166777	0.332375	0.135299	0.098564	0.027653	...	-0.230691	-0.074743	-0.217304	-0.182195	0.314457	-0.055558	-0.058298	-0.102007	-0.107342	0.101480
compactness_se	0.033961	0.292999	0.206000	0.191975	0.250744	0.212583	0.318943	0.738722	0.670279	0.490424	...	0.204607	0.143003	0.260516	0.199371	0.227394	0.678780	0.639147	0.483208	0.277878	0.590973
concavity_se	0.055239	0.253730	0.194204	0.143293	0.228082	0.207660	0.248396	0.570517	0.691270	0.439167	...	0.186904	0.100241	0.226680	0.188353	0.168481	0.484858	0.662564	0.440472	0.197788	0.439329
concave points_se	0.078768	0.408042	0.376169	0.163851	0.407217	0.372320	0.380676	0.642262	0.683260	0.615634	...	0.358127	0.086741	0.394999	0.342271	0.215351	0.452888	0.549592	0.602450	0.143116	0.310655
symmetry_se	-0.017306	-0.006522	-0.104321	0.009127	-0.081629	-0.072497	0.200774	0.229977	0.178009	0.095351	...	-0.128121	-0.077473	-0.103753	-0.110343	-0.012662	0.060255	0.037119	-0.030413	0.389402	0.078079
fractal_dimension_se	0.025725	0.077972	-0.042641	0.054458	-0.005523	-0.019887	0.283607	0.507318	0.449301	0.257584	...	-0.037488	-0.003195	-0.001000	-0.022736	0.170568	0.390159	0.379975	0.215204	0.111094	0.591328
radius_worst	0.082405	0.776454	0.969539	0.352573	0.969476	0.962746	0.213120	0.535315	0.688236	0.830318	...	1.000000	0.359921	0.993708	0.984015	0.216574	0.475820	0.573975	0.787424	0.243529	0.093492
texture_worst	0.064720	0.456903	0.297008	0.912045	0.303038	0.287489	0.036072	0.248133	0.299879	0.292752	...	0.359921	1.000000	0.365098	0.345842	0.225429	0.360832	0.368366	0.359755	0.233027	0.219122
perimeter_worst	0.079986	0.782914	0.965137	0.358040	0.970387	0.959120	0.238853	0.590210	0.729565	0.855923	...	0.993708	0.365098	1.000000	0.977578	0.236775	0.529408	0.618344	0.816322	0.269493	0.138957
area_worst	0.107187	0.733825	0.941082	0.343546	0.941550	0.959213	0.206718	0.509604	0.675987	0.809630	...	0.984015	0.345842	0.977578	1.000000	0.209145	0.438296	0.543331	0.747419	0.209146	0.079647
smoothness_worst	0.010338	0.421465	0.119616	0.077503	0.150549	0.123523	0.805324	0.565541	0.448822	0.452753	...	0.216574	0.225429	0.236775	0.209145	1.000000	0.568187	0.518523	0.547691	0.493838	0.617624
compactness_worst	-0.002968	0.590998	0.413463	0.277830	0.455774	0.390410	0.472468	0.865809	0.754968	0.667454	...	0.475820	0.360832	0.529408	0.438296	0.568187	1.000000	0.892261	0.801080	0.614441	0.810455
concavity_worst	0.023203	0.659610	0.526911	0.301025	0.563879	0.512606	0.434926	0.816275	0.884103	0.752399	...	0.573975	0.368366	0.618344	0.543331	0.518523	0.892261	1.000000	0.855434	0.532520	0.686511
concave points_worst	0.035174	0.793566	0.744214	0.295316	0.771241	0.722017	0.503053	0.815573	0.861323	0.910155	...	0.787424	0.359755	0.816322	0.747419	0.547691	0.801080	0.855434	1.000000	0.502528	0.511114
symmetry_worst	-0.044224	0.416294	0.163953	0.105008	0.189115	0.143570	0.394309	0.510223	0.409464	0.375744	...	0.243529	0.233027	0.269493	0.209146	0.493838	0.614441	0.532520	0.502528	1.000000	0.537848
fractal_dimension_worst	-0.029866	0.323872	0.007066	0.119205	0.051019	0.003738	0.499316	0.687382	0.514930	0.368661	...	0.093492	0.219122	0.138957	0.079647	0.617624	0.810455	0.686511	0.511114	0.537848	1.000000

	param_n_estimators	param_criterion	param_min_samples_split	param_max_features	mean_test_score
0	40	gini	4	log2	0.957756
1	100	gini	5	auto	0.950780
2	40	entropy	3	auto	0.960109